[2603.25638] Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers

Computer Science > Computation and Language arXiv:2603.25638 (cs) [Submitted on 26 Mar 2026] Title:Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers Authors:Mingmeng Geng, Yuhang Dong, Thierry Poibeau View a PDF of the paper titled Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers, by Mingmeng Geng and 2 other authors View PDF HTML (experimental) Abstract:Through an analysis of arXiv papers, we report several shifts in word usage that are likely driven by large language models (LLMs) but have not previously received sufficient attention, such as the increased frequency of "beyond" and "via" in titles and the decreased frequency of "the" and "of" in abstracts. Due to the similarities among different LLMs, experiments show that current classifiers struggle to accurately determine which specific model generated a given text in multi-class classification tasks. Meanwhile, variations across LLMs also result in evolving patterns of word usage in academic papers. By adopting a direct and highly interpretable linear approach and accounting for differences between models and prompts, we quantitatively assess these effects and show that real-world LLM usage is heterogeneous and dynamic. Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Digital Libraries (cs.DL); Machine Learning (cs.LG) Cite as: arXiv:2603.25638 [cs.CL]   (or arX...